We build AI and LLM capabilities into applications that people already use — where the value shows up in a workflow rather than in a demo. We also do the data work underneath: analytics, reporting and the pipelines that feed them.
LLM features in production
Integrating language models into an existing product raises questions a prototype never has to answer: what happens when the model is wrong, who reviews the output, what data leaves the system, and what it costs per user. We build with those answered.
Keeping a person in control
Our own product is built on the principle that AI should reduce work rather than add a system to supervise. In practice that means AI drafts and suggests, and a person retains the decision.